Wireless communication with enhanced maximum permissible exposure (MPE) compliance

ABSTRACT

Aspects of the disclosure relate to classifying a target object. An electronic device may transmit a detection signal and receive a reflection signal reflected from the target object. The electronic device then determines, based on one or more features of the reflection signal, a category of the target object and adjusts at least one transmission parameter based on the category. The electronic device then transmits an adjust signal using the transmission parameter. Other aspects, embodiments, and features are also claimed and described.

RELATED MATTER

This application for patent is related to U.S. Patent Application No.62/890,514, titled, “WIRELESS COMMUNICATION WITH ENHANCED MAXIMUMPERMISSIBLE EXPOSURE (MPE) COMPLIANCE BASED ON VITAL SIGNS DETECTION,”filed on the same day as this application and incorporated herein byreference in its entirety.

TECHNICAL FIELD

The technology discussed below relates generally to wirelesscommunication and/or object classification systems, and moreparticularly, to machine-learning-based wireless emissions control aswell as object classification for controlling maximum permissibleexposure. Embodiments can provide and enable techniques for classifyingnearby subjects and/or target objects (e.g., those detected by awireless proximity sensor or other communication-enabling components)and controlling for maximum permissible exposure.

INTRODUCTION

Next-generation wireless telecommunication systems (e.g., such as FifthGeneration (5G) or New Radio (NR) technologies) are being deployedutilizing millimeter-wave (mmW) signals. These signals can operate, forexample, at a 28 GHz and 39 GHz spectrum. Although higher frequencysignals provide larger bandwidths to efficiently communicate vastamounts of information/data, mmW signals may suffer from high path loss(e.g., path attenuation). To compensate for path loss, transmit powerlevels can be increased, or beamforming can concentrate energy in aparticular direction.

As with various types of electronic signal transmissions, there areusually regulatory rules governing transmission strengths. For example,for mmW signals, the US Federal Communications Commission (FCC) andother regulatory bodies set stringent RF exposure requirements. Theserules ensure that the maximum permissible exposure (MPE) on human skindoes not exceed a power density of 1 mW/cm². To meet targetedguidelines, electronic devices are responsible for balancing performancewith transmission power and other constraints. This balancing act can bechallenging to achieve, especially with devices that have cost, size,and other concerns.

BRIEF SUMMARY OF SOME EXAMPLES

The following presents a simplified summary of one or more aspects ofthe present disclosure, in order to provide a basic understanding ofsuch aspects. This summary is not an extensive overview of allcontemplated features of the disclosure, and is intended neither toidentify key or critical elements of all aspects of the disclosure norto delineate the scope of any or all aspects of the disclosure. Its solepurpose is to present some concepts of one or more aspects of thedisclosure in a simplified form as a prelude to the more detaileddescription that is presented later.

According to some aspects, wireless communication devices, methods, andsystems are provided to enable MPE compliance and/or human target objectawareness and detection. For example, a device embodiment (e.g. a mobileapparatus) may include a wireless communication enabling component(e.g., an mmW signal interface). A wireless communication enablingcomponent (e.g., a transceiver) can not only facilitate wirelesscommunication via receipt and transfer of radio frequency signals (e.g.,mmW signals), a transceiver can also leverage mmW signaling to detectobjects. A device embodiment can utilize object detection features todetermine classes of objects. If objects are determined to be human,non-human, animate, inanimate, etc., the device can adjust operatingparameters of a communication interface (e.g., a mmW transceiver) forMPE compliance (e.g., power up or power down signal transmissions).According to some aspects, signal transmission power adjustments canoccur in real time or according to a variety of desired timingarrangements.

In some aspects the disclosure provides a method for classification of atarget object. The method includes transmitting a detection signal andreceiving a reflection signal reflected from the target object. Themethod further includes determining, based on one or more features ofthe reflection signal, a category of the target object, and adjusting atleast one transmission parameter based on the category of the targetobject. The method further includes transmitting an adjusted signalusing the transmission parameter.

In further aspects, the disclosure provides an electronic deviceconfigured for classification of a target object. The electronic deviceincludes a processor, a transceiver communicatively coupled to theprocessor, and a data storage medium communicatively coupled to theprocessor. Here, the processor is configured for transmitting adetection signal via the transceiver and receiving a reflection signalvia the transceiver, the reflection signal reflected from the targetobject. The processor is further configured for determining, based onone or more features of the reflection signal, a category of the targetobject and adjusting at least one transmission parameter based on thecategory of the target object. The processor is further configured fortransmitting, via the transceiver, an adjusted signal using thetransmission parameter.

In further aspects, the disclosure provides an electronic deviceconfigured for classification of a target object. The electronic deviceincludes means for transmitting a detection signal and means forreceiving a reflection signal, the reflection signal reflected from thetarget object. The electronic device further includes means fordetermining, based on one or more features of the reflection signal, acategory of the target object, and means for adjusting at least onetransmission parameter based on the category of the target object. Theelectronic device further includes means for transmitting an adjustedsignal using the transmission parameter.

In further aspects, the disclosure provides a non-transitory computerreadable medium storing computer executable code. The code includesinstructions for causing an electronic device to transmit a detectionsignal and instructions for causing the electronic device to receive areflection signal reflected from the target object. The code furtherincludes instructions for causing the electronic device to determine,based on one or more features of the reflection signal, a category ofthe target object, and instructions for causing the electronic device toadjust at least one transmission parameter based on the category of thetarget object. The code further includes instructions for causing theelectronic device to transmit an adjusted signal using the transmissionparameter.

In further aspects, the disclosure provides a wireless communicationdevice including a housing shaped and sized to carry one or morecomponents, including a memory, the wireless transceiver, a poweramplifier, and at least one processor. The wireless transceiver isconfigured to transmit and/or receive millimeter wave signals via awireless channel. The wireless transceiver is further configured tosense objects relative to and exterior the housing via millimeter wavesignaling, and configured to provide object-sensing information to theat least one processor. And the at least one processor is configured tocontrol the power amplifier to moderate a transmission parameterassociated with the wireless transceiver transmitting and/or receivingmillimeter waves based on the object-sensing information, and configuredto convey information associated with sensing objects positioningrelative to and exterior the housing.

In further aspects, the disclosure provides, in a system for providinginformation between a plurality of wireless communication devices, theinformation capable of assessing an object class of an observed object,a method of providing information to a wireless communication device.Here, the method includes configuring a data store to be in electricalwireless communication with one or more unique wireless communicationdevices among the plurality of wireless communication devices operatingwithin a wireless network. The method further includes receivingmicromovement information from the one or more unique wirelesscommunication devices transmitted via a wireless network, themicromovement information including data observances indicatingmicromovements associated with one or more target objects. The methodfurther includes determining object-class information for one or moretarget objects based at least in part on received micromovementinformation and other stored information. The method further includestransmitting the object-class information to one or more of the wirelesscommunication devices in the wireless network such that any one of thewireless communication devices can moderate a wireless transmissionparameter associated with its transmission and reception operations.

In further aspects, the disclosure provides a wireless communicationdevice configured as a vehicle including a vehicle body configured tocarry at least one of a payload or a passenger. The vehicle includes awireless communication interface sized and shape to be placed in alocation proximate or within the vehicle body, where the wirelesscommunication interface is configured to transmit and/or receivemillimeter wave signals via a wireless channel. The wirelesscommunication interface is further configured to sense objects relativeto the vehicle body via millimeter wave signaling and configured toprovide object-sensing information to at least one processor. The atleast one processor is configured to control a transmission parameterassociated with the wireless communication interface transmitting and/orreceiving millimeter waves based on the object-sensing information, andconfigured to convey information associated with sensing objectsrelative to the vehicle body.

In further aspects, the disclosure provides a wireless communicationdevice configured for gaming, where the wireless communication deviceincludes a housing sized and shaped for gaming to allow a user toparticipate in an electronic gaming environment. The wirelesscommunication device includes a wireless communication interface sizedand shaped to be placed in a location proximate or within the housing,where the wireless communication interface is configured to transmitand/or receive millimeter wave signals via a wireless channel. Thewireless communication interface is further configured to sense objectsrelative to the housing via millimeter wave signaling, and configured toprovide object-sensing information to at least one processor. The atleast one processor is configured to control a transmission parameterassociated with the wireless communication interface transmitting and/orreceiving millimeter waves based on the object-sensing information andconfigured to convey information associated with sensing objectsrelative to the housing.

These and other aspects of the invention will become more fullyunderstood upon a review of the detailed description, which follows.Other aspects, features, and embodiments will become apparent to thoseof ordinary skill in the art, upon reviewing the following descriptionof specific, exemplary embodiments in conjunction with the accompanyingfigures. While features may be discussed relative to certain embodimentsand figures below, all embodiments can include one or more of theadvantageous features discussed herein. In other words, while one ormore embodiments may be discussed as having certain advantageousfeatures, one or more of such features may also be used in accordancewith the various embodiments discussed herein. In similar fashion, whileexemplary embodiments may be discussed below as device, system, ormethod embodiments it should be understood that such exemplaryembodiments can be implemented in various devices, systems, and methods.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a wireless electronic device according tosome aspects of the disclosure.

FIG. 2 is a schematic diagram of an operating environment for anelectronic device utilizing a radar-based proximity detector accordingto some aspects of the disclosure.

FIG. 3 is a block diagram illustrating additional detail of a portion ofan electronic device according to some aspects of the disclosure.

FIG. 4 is a series of charts showing radar echo signatures of differenttarget objects according to some aspects of the disclosure.

FIG. 5 is a series of charts showing two-dimensional feature spaces thatillustrate how target object classification can be achieved based on theuse of suitable features according to some aspects of the disclosure.

FIG. 6 is a chart showing how a service vector machine (SVM) canestablish an optimal boundary separating different categories of targetobjects according to some aspects of the disclosure.

FIG. 7 is a flow chart illustrating an exemplary process for building atarget object classifier according to some aspects of the disclosure.

FIG. 8 is a flow chart illustrating an exemplary process for utilizing atarget object classifier to control one or more transmission parametersaccording to some aspects of the disclosure.

DETAILED DESCRIPTION

The detailed description set forth below in connection with the appendeddrawings is intended as a description of various configurations and isnot intended to represent the only configurations in which the conceptsdescribed herein may be practiced. The detailed description includesspecific details for the purpose of providing a thorough understandingof various concepts. However, it will be apparent to those skilled inthe art that these concepts may be practiced without these specificdetails. In some instances, well known structures and components areshown in block diagram form in order to avoid obscuring such concepts.

While aspects and embodiments are described in this application byillustration to some examples, those skilled in the art will understandthat additional implementations and use cases may come about in manydifferent arrangements and scenarios. Innovations described herein maybe implemented across many differing platform types, devices, systems,shapes, sizes, packaging arrangements. For example, embodiments and/oruses may come about via integrated chip embodiments and othernon-module-component based devices (e.g., end-user devices, vehicles,communication devices, computing devices, industrial equipment,retail/purchasing devices, medical devices, AI-enabled devices, etc.).While some examples may or may not be specifically directed to use casesor applications, a wide assortment of applicability of describedinnovations may occur. Implementations may range a spectrum fromchip-level or modular components to non-modular, non-chip-levelimplementations and further to aggregate, distributed, or OEM devices orsystems incorporating one or more aspects of the described innovations.In some practical settings, devices incorporating described aspects andfeatures may also necessarily include additional components and featuresfor implementation and practice of claimed and described embodiments.For example, transmission and reception of wireless signals necessarilyincludes a number of components for analog and digital purposes (e.g.,hardware components including antenna, RF-chains, power amplifiers,modulators, buffer, processor(s), interleaver, adders/summers, etc.). Itis intended that innovations described herein may be practiced in a widevariety of devices, chip-level components, systems, distributedarrangements, end-user devices, etc. of varying sizes, shapes andconstitution.

An electronic wireless communication device that utilizes millimeterwave (mmW) signals may use a high transmit power to compensate for pathloss associated with signals at these frequencies. Many of theseelectronic devices, such as a mobile user equipment (UE), can bephysically operated by a user. Close physical proximity to such anelectronic device presents opportunities for radiation to exceed a givenguideline, such as a maximum permitted exposure (MPE) limit asdetermined by the Federal Communications Commission (FCC) or otherregulatory body. Because of these issues, it is advantageous to enabledevices to moderate one or more transmission parameters, including butnot limited to transmit power, based on a proximity of the user.

Some proximity detection techniques may use a dedicated sensor, such asa camera, an infrared (IR) sensor, a radar sensor, etc. to detect a userHowever, these sensors may be bulky and expensive. Furthermore, a singleelectronic device can include multiple antennas that are positioned ondifferent surfaces (e.g., on a top, a bottom, or opposite sides). Toaccount for each of these antennas, according to some aspects, multiplecameras or sensors may need to be installed near each of these antennas,which further increases a cost and size of the electronic device.

In a further aspect and/or example, the same wireless transceiverutilized for wireless communication can also perform proximitydetection. For example, local oscillator (LO) circuitry within awireless transceiver can generate one or more reference signals that canenable both proximity detection and wireless communication. The LOcircuitry can enable a frequency-modulated continuous wave (FMCW) signalor a multi-tone signal to be transmitted for radar-based proximitydetection. By analyzing reflections from either of these signals, arange (e.g., distance or slant range) to an object, and in someexamples, a material composition of the object can be determined.

To ensure compliance with MPE requirements, according to some aspects, aproximity detector, including but not limited to an integratedFMCW-based radar function, can detect the presence of objects and/ornearby targets. Objects may be located around a device, and some objectsmay be targets of interest. For example, a detector can determinewhether a target is within 20 cm of a device's radiating elements.Detection of multiple objects can be used to create a virtual map ofitems or subjects having spatial relationships to a device. Based onsuch proximity detection, a device can accordingly adjust one or moretransmission parameters used for wireless communication, such as byreducing a transmission power, by switching to a different transmitantenna, etc. By actively measuring the range to one or more objects, anelectronic device can continually monitor its surrounding environment,and can incrementally adjust one or more transmission parameters toaccount for the object's movement (e.g., adjustments can increase ordecrease transmission power generally or in particular directions viabeamformed mmWaves or RF waves).

In general, radar signal processing is tailored to extract a target'slocation-based information such as its distance, speed, angle, position,etc. However, a typical radar does not provide information about thenature of the target such as whether the target is a livinganimal/being, human, or not. According to an aspect of the presentdisclosure, it can be advantageous to adjust one or more transmissionparameters used for wireless communication based not only on proximitydetection of a nearby target, but in addition, based on a classificationof the target. That is, MPE requirements generally only apply toexposure to human beings. If an inanimate object such as a coffee mug ora wall is in close proximity to an electronic device, then MPErequirements may not apply and a high transmission parameter (forexample) can continue to be utilized. Therefore, including informationabout objects spaced away in spatial locations (e.g., a nearby target orsubject located removed from the electronic device), or a classificationof the detected proximate target, can help optimize the powertransmission of the electronic device.

FIG. 1 illustrates an example electronic device 102 for implementing amachine learning (ML) algorithm to categorize target objects detectedutilizing a radar-based proximity detector according to some aspects ofthis disclosure. In an example environment 100, the electronic device102 communicates with a base station 104 through a wirelesscommunication link 106 (wireless link 106). For example, the electronicdevice 102 and the base station 104 may be a part of a system forproviding information between a plurality of unique wirelesscommunication devices. In FIG. 1, the electronic device 102 isillustrated as a smart phone, a vehicle, or a gaming device, to providesome examples. However, an electronic device 102 may be any suitablestationary or mobile apparatus that includes a wireless transceiver.Within the present disclosure, the term electronic device broadly refersto a diverse array of devices and technologies. Electronic devices mayinclude a number of hardware structural components sized, shaped, andarranged to help in communication; such components can include antennas,antenna arrays, RF chains, amplifiers, one or more processors, etc.electrically coupled to each other. For example, some non-limitingexamples of a mobile apparatus include a mobile, a cellular (cell)phone, a smart phone, a session initiation protocol (SIP) phone, alaptop, a personal computer (PC), a notebook, a netbook, a smartbook, atablet, a personal digital assistant (PDA), and a broad array ofembedded systems, e.g., corresponding to an “Internet of things” (IoT).A mobile apparatus may additionally be an automotive or othertransportation vehicle, a remote sensor or actuator, a robot or roboticsdevice, a satellite radio, a global positioning system (GPS) device, anobject tracking device, a drone, a multi-copter, a quad-copter, a remotecontrol device, a consumer and/or wearable device, such as eyewear, awearable camera, a virtual reality device, a smart watch, a health orfitness tracker, a digital audio player (e.g., MP3 player), a camera, agame console, a gaming device (e.g. an interface enabling a user toparticipate or play in an electronic game), etc. A mobile apparatus mayadditionally be a digital home or smart home device such as a homeaudio, video, and/or multimedia device, an appliance, a vending machine,intelligent lighting, a home security system, a smart meter, anaugmented reality device, a virtual reality device, a mixed realitydevice, etc. A mobile apparatus may additionally be a smart energydevice, a security device, a solar panel or solar array, a municipalinfrastructure device controlling electric power (e.g., a smart grid),lighting, water, etc.; an industrial automation and enterprise device; alogistics controller; agricultural equipment; military defenseequipment, vehicles, aircraft, ships, and weaponry, etc. Still further,a mobile apparatus may provide for connected medicine or telemedicinesupport, e.g., health care at a distance. Telehealth devices may includetelehealth monitoring devices and telehealth administration devices,whose communication may be given preferential treatment or prioritizedaccess over other types of information, e.g., in terms of prioritizedaccess for transport of critical service data, and/or relevant QoS fortransport of critical service data. In some examples, the electronicdevice 102 may be a wireless communication device including a housingshaped and sized to carry one or more components, such as thosedescribed below.

In one example, the electronic device 102 may be, or may be a part of, avehicle that includes a vehicle body configured to carry at least one ofa payload or a passenger. In this example, the wireless transceiver 120may be sized and shaped to be placed in a location proximate to and/orwithin the vehicle body. In another example, the electronic device 102may be, or may be a part of, a gaming device that includes a housingsized and shaped to allow a user to participate in an electronic gamingenvironment. In this example, the wireless transceiver 120 may be sizedand shaped to be placed in a location proximate the housing. Further,the gaming device may include a visible interface field defining avisual display configured to visually convey object sensing informationto the user, and/or one or more user interfaces positioned proximate thehousing, for receiving user input, and in response, conveying objectsensing information to the user.

The base station 104 communicates with the electronic device 102 via thewireless link 106, which may be implemented as any suitable type ofwireless link. Although depicted as a tower of a cellular network, thebase station 104 may represent or be implemented as another device, suchas a satellite, cable television head-end, terrestrial televisionbroadcast tower, access point, peer-to-peer device, mesh network node,small cell node, fiber optic line, and so forth. Therefore, theelectronic device 102 may communicate with the base station 104 oranother device via a wired connection, a wireless connection, or acombination thereof.

The wireless link 106 can include a downlink of data or controlinformation communicated from the base station 104 to the electronicdevice 102 and an uplink of other data or control informationcommunicated from the electronic device 102 to the base station 104. Thewireless link 106 may be implemented using any suitable communicationprotocol or standard, such as 3rd Generation Partnership ProjectLong-Term Evolution (3GPP LTE), 5th Generation New Radio (5G NR), IEEE802.11, IEEE 802.16, Bluetooth™, and so forth. In some implementations,instead of or in addition to providing a data link, the wireless link106 may wirelessly provide power and the base station 104 may include apower source.

The electronic device 102 includes an application processor 108 and acomputer-readable storage medium 110 (CRM 110). The applicationprocessor 108 may include any type of processor (e.g., an applicationprocessor, a digital signal processor (DSP), or a multi-core processor),that executes processor-executable code stored by the CRM 110. The CRM110 may include any suitable type of data storage media, such asvolatile memory (e.g., random access memory (RAM)), non-volatile memory(e.g., Flash memory), optical media, magnetic media (e.g., disk ortape), and so forth. In the context of this disclosure, the CRM 110 isimplemented to store instructions 112, data 114, and other informationand software of the electronic device 102. For example, the CRM 110 mayinclude memory for storing data configured to enable the processor/DSP128 to process object-sensing information against stored data, therebyenabling classification of target objects into different object-typeclasses. The CRM 110 may reside in the application processor 108,external to the application processor 108, or distributed acrossmultiple entities including the application processor 108. The CRM 110may be embodied in a computer program product. By way of example, acomputer program product may include a computer-readable medium inpackaging materials. Those skilled in the art will recognize how best toimplement the described functionality presented throughout thisdisclosure depending on the particular application and the overalldesign constraints imposed on the overall system.

One or more processors 108 may execute software. Software shall beconstrued broadly to mean instructions, instruction sets, code, codesegments, program code, programs, subprograms, software modules,applications, software applications, software packages, routines,subroutines, objects, executables, threads of execution, procedures,functions, etc., whether referred to as software, firmware, middleware,microcode, hardware description language, or otherwise.

The electronic device 102 may also include input/output ports 116 (I/Oports 116) and a display 118. The I/O ports 116 enable data exchanges orinteraction with other devices, networks, or users. The I/O ports 116may include serial ports (e.g., universal serial bus (USB) ports),parallel ports, audio ports, infrared (IR) ports, and so forth. Thedisplay 118 presents graphics of the electronic device 102, such as auser interface associated with an operating system, program, orapplication. Alternately or additionally, the display 118 may beimplemented as a display port or virtual interface, through whichgraphical content of the electronic device 102 is presented.

A wireless transceiver 120 of the electronic device 102 may include awireless transmitter 122 and a wireless receiver 124. The wirelesstransceiver 120 provides connectivity to respective networks and otherelectronic devices connected therewith. Additionally, the electronicdevice 102 may include a wired transceiver, such as an Ethernet or fiberoptic interface for communicating over a local network, intranet, or theInternet. The wireless transceiver 120 may facilitate communication overany suitable type of wireless network, such as a wireless LAN (WLAN),peer-to-peer (P2P) network, mesh network, cellular network, wirelesswide-area-network (WWAN), and/or wireless personal-area-network (WPAN).In the context of the example environment 100, the wireless transceiver120 enables the electronic device 102 to communicate with the basestation 104 and networks connected therewith.

The wireless transceiver 120 includes circuitry and logic fortransmitting and receiving signals via antennas 126. For example, thewireless transceiver 120 may be configured to transmit and/or receivemmW signals via wireless channel, and further, to sense objects relativeto, and exterior to, the housing of the electronic device 192 byutilizing mmW signaling. The wireless transceiver 120 may be configuredto engage in mmW communication and mmW object sensing substantiallysimultaneously. Further, the wireless transceiver 120 may be configuredto sense objects ranging from about 1 to about 360 degrees, such thatthe processor/DSP 128 can yield an object-sensing map of objectsexterior to the housing of the electronic device 102.

The wireless transceiver 120 includes circuitry and logic fortransmitting and receiving signals via antennas 126. Components of thewireless transceiver 120 can include amplifiers, mixers, switches,analog-to-digital converters, filters, and so forth for conditioningsignals. The wireless transceiver 120 may also include logic to performin-phase/quadrature (I/Q) operations, such as synthesis, encoding,modulation, decoding, demodulation, and so forth. The wirelesstransceiver 120 may include one or more components or features foradjusting or controlling transmission parameters. For example, thewireless transceiver 120 may provide object-sensing information to theprocessor DSP 128. In some cases, components of the wireless transceiver120 are implemented as separate transmitter 122 and receiver 124entities. Additionally or alternatively, the wireless transceiver 120can be realized using multiple or different sections to implementrespective transmitting and receiving operations (e.g., separatetransmit and receive chains). Although the examples described belowgenerally refer to an integrated wireless transceiver 120 that performsboth wireless communication and object sensing operations, aspects ofthe present disclosure are not limited to this case. For example, theelectronic device 102 may include an interface circuit for interfacingwith an auxiliary and/or auxiliary sensing device, spaced apart from thehousing of the electronic device 102. Auxiliary and/or auxiliary sensingdevices can include remote wireless devices capable of communicatingwith the electronic device 102 (e.g., gaming controller, wearable,augmented/virtual reality device, and other types of mobile devicesdescribed above). Here, the interface circuit (not illustrated) canenable communication between the electronic device 102 and the auxiliarysensing device, such that the electronic device 102 receivesobject-sensing information from the auxiliary sensing device. Inresponse to the object-sensing information, the electronic device 102may moderate a transmission parameter associated with the wirelesstransceiver 120 transmitting and/or receiving mmW signals. Moderation oftransmission power may include controlling and/or modifying transmissionpower, such as increasing and/or decreasing or otherwise changingtransmission power levels.

The electronic device 102 also includes a processor/digital signalprocessor (DSP) 128, which is coupled to the wireless transceiver 120.The processor/DSP 128, which may include a modem, can be implementedwithin or separate from the wireless transceiver 120. Although notexplicitly shown, the processor/DSP 128 can include a portion of the CRM110 or can access the CRM 110 to obtain computer-readable instructions.The processor/DSP 128 controls the wireless transceiver 120 and enableswireless communication or proximity detection to be performed. Forexample, the processor/DSP 128 may control a power amplifier at thetransceiver 120 to moderate a transmission parameter based onobject-sensing information. The processor/DSP 128 can include basebandcircuitry to perform high-rate sampling processes that can includeanalog-to-digital conversion, digital-to-analog conversion, Fouriertransforms, gain correction, skew correction, frequency translation, andso forth. The processor/DSP 128 can provide communication data to thewireless transceiver 120 for transmission. The processor/DSP 128 canalso process a baseband version of a signal obtained from the wirelesstransceiver 120 to generate data, which can be provided to other partsof the electronic device 102 via a communication interface for wirelesscommunication or proximity detection.

In some examples, the electronic device 102 (e.g., via the processor/DSP128) may be configured to generate, output, or produce a map. A map canbe based at least on the object-sensing information. A map can includespatial location and other information associated with objectssurrounding the electronic device 102 (e.g., communication status,operational status, relative object location, direction, movement, typeand/or class). In addition, a map can be used to display and/or identifyobjects, humans, and/or animals relative to the electronic device 102. Amap may be time static or may be time dynamic. A map can be used tovisually display objects around a device ranging from a full 360-degreearrangement as well as other or smaller ranges (e.g., from about 1degree to about 360 degrees). A map can enable a user to access anaugmented/virtual reality environment (e.g., (e)gaming, (tele)health,and/or educational). According to some aspects, a map may be provided asan output to a user via a display screen (e.g. display screen shown forelectronic device 102) or other user interface. The electronic device102 may transmit map information to other devices in the network.According to some aspects, sharing map information in this manner canaid in spreading map and/or spatial positioning information within anetwork.

Although not explicitly depicted, the wireless transceiver 120 or theprocessor/DSP 128 can also include a controller. The controller caninclude at least one processor and at least one CRM, such as theapplication processor 108 and the CRM 110. The CRM can storecomputer-executable instructions, such as the instructions 112. Theprocessor and the CRM can be localized at one module or one integratedcircuit chip or can be distributed across multiple modules or chips.Together, a processor and associated instructions can be realized inseparate circuitry, fixed logic circuitry, hard-coded logic, and soforth. The controller can be implemented as part of the wirelesstransceiver 120, the processor/DSP 128, a special-purpose processorconfigured to perform MPE techniques, a general-purpose processor, somecombination thereof, and so forth.

The processor/DSP 128 may include feature extraction circuitry 130 andSVM classification circuitry 132. The feature extraction circuitry 130may be utilized for extracting features of a reflection signal,indicative of micromovements characteristic of a human target. The SVMclassification circuitry 132 may be utilized for determining a categoryor classification of a target object, based on one or more extractedfeatures of the reflection signal. For example, the SVM classificationcircuitry 132 may apply the extracted features to a classificationmodel, wherein the SVM classification circuitry 132 determines alocation of the target object, within a feature space, relative to aboundary that separates objects within the feature space intocategories. Based on this location, the SVM classification circuitry 132may identify the category of the target object, including human tissue,non-human objects, or a combination thereof.

In some examples, the base station 104 may be a data store, or may be incommunication with one or more data stores that receive micromovementinformation from wireless communication devices via the wirelessnetwork. In this manner, a data store may communicate objectclassification information between wireless communication devices in thenetwork. Based on this information, the data store may determine objectclass information for one or more target objects based at least in parton received micromovement information and, in some examples, otherstored information. This object class information may then becommunicated to other wireless communication devices such that any oneof the wireless communication devices in the network can moderate awireless transmission parameter associated with its transmission andreception operations. In a further example, the data store maycommunicate information indicating that any one or more of the wirelesscommunication devices has moderated its power transmission levels, e.g.,based on target object classification.

FIG. 2 illustrates an example operating environment 200 for categorizingtarget objects detected utilizing a radar based proximity detector. Inthe example environment 200, a hand 214 of a user holds the electronicdevice 102. In one aspect, the electronic device 102 communicates withthe base station 104 by transmitting an uplink signal 202 (UL signal202) or receiving a downlink signal 204 (DL signal 204) via at least oneof the antennas 126. A user's thumb, however, may represent a proximatetarget object 206 that may be exposed to radiation via the uplink signal202. To determine a range to the target object 206, the electronicdevice 102 transmits a proximity detection signal 208-1 via at least oneof the antennas 126 and receives a reflected proximity detection signal208-2 via at least another one of the antennas 126.

In one implementation, the proximity detection signal 208-1 includes afrequency-modulated continuous-wave (FMCW) signal 216. In general, afrequency of the FMCW signal 216 increases or decreases across a timeinterval. Different types of frequency modulations may be used,including linear-frequency modulations (LFM) (e.g., chirp),sawtooth-frequency modulations, triangular-frequency modulations, and soforth. The FMCW signal 216 enables radar-based ranging techniques to beutilized to determine the range to the target object 206. To achieve afiner range resolution (e.g., on the order of centimeters (cm)) forclose-range applications, larger bandwidths can be utilized, such as 1gigahertz (GHz), 4 GHz, 8 GHz, and so forth. For instance, the FMCWsignal 216 can have a bandwidth of approximately 4 GHz and includefrequencies between approximately 26 and 30 GHz. The finer rangeresolution improves range accuracy and enables multiple objects 206 tobe distinguished in range. The FMCW signal 216 can provide an accuraterange measurement for a variety of distances based on the bandwidth(e.g., between approximately 4 and 20 cm for a 4 GHz bandwidth). Anamount of time for performing proximity detection can also be relativelyshort using the FMCW signal 216, such as within approximately onemicrosecond.

In another implementation, the proximity detection signal 208 may be amulti-tone signal 218, which includes at least three tones (e.g.,frequencies). The multi-tone signal 218 can be generated using existingcomponents within the wireless transceiver 120, which are also used togenerate the uplink signal 202. For example, the multi-tone signal 218can be generated using an existing phase lock loop (PLL), usingOrthogonal Frequency-Division Multiplexing (OFDM), or using a multi-tonetransmit signal generated at baseband via a digital signal generator.Depending on the technique used, an amount of time for performingproximity detection via the multi-tone signal 218 can be on the order ofapproximately one microsecond and 400 microseconds. Frequencyseparations between the tones can be on the order of megahertz (MHz) orGHz. A bandwidth of the multi-tone signal 218 can be, for example,approximately 800 MHz or 2 GHz. The range to the object 206 isdetermined by analyzing a change in phase across each of these tones. Toimprove range accuracy, larger bandwidths (e.g., separations betweentones) or larger quantities of tones can be used. The multi-tone signal218 can be used to measure ranges between approximately 0 and 7 cm.

In some electronic devices 102, the antennas 126 may include at leasttwo different antennas, at least two antenna elements 212 of an antennaarray 210, at least two antenna elements 212 associated with differentantenna arrays 210, or any combination thereof. As shown in FIG. 2, theantennas 126 correspond to the antenna elements 212 within the antennaarray 210, which can include multiple antenna elements 212-1 to 212-N,where N represents a positive integer. Using at least one of the antennaelements 212, the wireless transceiver 120 can transmit the proximitydetection signal 208-1 while receiving the reflected proximity detectionsignal 208-2 using at least another one of the antenna elements 212. Inother words, the wireless transceiver 120 can receive the reflectedproximity detection signal 208-2 via a first antenna element 212-1during a portion of time that the proximity detection signal 208-1 istransmitted via a second antenna element 212-2. The antennas 124 and/orelements thereof may be implemented using any type of antenna, includingpatch antennas, dipole antennas, and so forth.

If the electronic device 102 includes multiple antennas 126 located ondifferent sides of the electronic device 102 (e.g., a top, a bottom, oropposite sides), the described techniques can enable the user to bedetected with respect to each antenna 126. In this way, transmissionparameters can be independently adjusted relative to the range of theobject 206 with respect to each antenna 126. Such independent detectiontherefore enables the two or more of the antennas 126 to be configuredfor different purposes. For example, one of the antennas 126 can beconfigured for enhanced communication performance while another one ofthe antennas 126 is simultaneously configured to comply with FCCrequirements. As described in further detail with respect to FIG. 3,some of the components of the wireless transceiver 120 can be utilizedfor both wireless communication and proximity detection, either atdifferent times or simultaneously. In some examples, the electronicdevice may perform radar sensing and proximity detection during unusedtime slots of a wireless communication protocol. For example, in mmWcommunication, a communication frame may include one or more unusedslots for random channel access (RACH); the electronic device 102 mayperform radar sensing and proximity detection during these otherwiseunused RACH slots.

In some examples, when the electronic device 102 is operating in awireless communication network, the electronic device 102 maycommunicate object sending data to one or more other devices within thenetwork. In this manner, the electronic device 102 and the other devicescan share target object sensing data with one another. Accordingly theelectronic device 102 and the other devices may determine surroundingtarget object information of objects located around them.

FIG. 3 illustrates an example implementation of a wireless transceiver120 and processor/DSP circuitry 128 for a machine learning (ML)algorithm to categorize target objects detected utilizing a radar-basedproximity detector according to some aspects of this disclosure. Thewireless transceiver 120 may include a transmitter 122 and a receiver124, which are respectively coupled between the processor/DSP 128 and anantenna array 210. The transceiver 120 includes a power amplifier (PA)302 configured to dynamically provide power to selected ones of theantenna elements 210 for moderation of the transmission parameter and/orfor beamforming. The transceiver 120 further includes a low-noiseamplifier (LNA) 304 for amplifying a signal received by a receiveantenna 210-2. Local oscillator (LO) circuitry 306 is coupled to mixers308 and 310. The LO circuitry 306 generates at least one referencesignal, which enables the mixers 308 and 310 to upconvert or downconvertanalog signals within the transmit or receive chains, respectively. TheLO circuitry 306 may further be configured to generate one or moredifferent types of reference signals to support both proximity detectionand wireless communication. In some examples, the LO circuitry 306 maybe configured to generate one or more in-phase and quadrature (I/Q)reference signals. In this manner, the transmission from the transmitantenna 210-1 may include I and Q components. And further, after thereflected signal is received from the receive antenna 210-2, I and Qcomponents of the reflected signal may be separated from one another forprocessing.

The transceiver 120 can also include other additional components thatare not depicted in FIG. 3. These additional components can includeband-pass filters, additional mixers, switches, and so forth. Moreover,as discussed above, the transceiver 120 may be configured not only forthe target object ranging and detection described immediately below, butadditionally for wireless communication.

Although not explicitly depicted, the wireless transceiver 120 and/orthe processor/DSP 128 can also include a controller. The controller caninclude at least one processor and at least one CRM, such as theapplication processor 108 and the CRM 110. The CRM can storecomputer-executable instructions, such as the instructions 112. Theprocessor and the CRM can be localized at one module or one integratedcircuit chip or can be distributed across multiple modules or chips.Together, a processor and associated instructions can be realized inseparate circuitry, fixed logic circuitry, hard-coded logic, and soforth. The controller can be implemented as part of the wirelesstransceiver 120, the processor/DSP 128, a special-purpose processorconfigured to perform MPE techniques, a general-purpose processor, somecombination thereof, and so forth.

A voltage-controlled oscillator (VCO) 312 may be configured to generatea sinusoidal signal having a frequency that depends on a voltage of aninput signal v(t). That is, by properly varying the input signal v(t) tothe VCO 312, the VCO 312 may generate, for example, a sinusoid ofincreasing frequency over time, often called a chirp signal. This chirpsignal can be utilized for an FMCW-based radar. Of course, othersuitable input signals v(t), and other suitable radar configurations maybe utilized within the scope of this disclosure for proximity detectionand target object sampling.

The chirp signal may be amplified by the PA 302 and mixed with the LOsignal (i.e., upconverted) at the mixer 308 for transmission from atransmit antenna 210-1. The transmitted signal may reflect off a targetobject 314, being reflected back to a receive antenna 210-2. Thereflected signal at the receive antenna 210-2 may be mixed with the LOsignal (i.e., downconverted) at the mixer 310 and amplified by the LNA304.

The output of the LNA 304 (i.e., the amplified received signal) may bemixed with the chirp signal at a mixer 316. With an FMCW-based radar,this mixing creates a beat signal, which is representative of afrequency offset between the radio-frequency transmit signal and theradio-frequency receive signal. In general, the frequency of the beatsignal is proportional to the distance of the target object 314.

The beat signal may be processed by baseband circuitry 318, configuredto perform various baseband functions including but not limited to gaincorrection, skew correction, frequency translation, etc. The output fromthe baseband circuitry 318 may be converted to the digital domainutilizing one or more analog-to-digital converters (ADC) 320. In anexample wherein the radar transmission includes I and Q components, asdiscussed above, the output from the baseband circuitry 318 may includeseparate I and Q signals, and the ADC 320 may include two ADCs forrespectively converting each of the I and Q components to the digitaldomain. The digital output from the ADC 320 may then be provided to theprocessor/DSP circuitry 128. In some implementations, the processor/DSPcircuitry 128 may be a DSP or any suitable functional component forcarrying out the below-described processes.

An undesired side effect of having a closely located transmit antenna210-1 and receive antenna 210-2, as may occur in a small electronicdevice, is mutual coupling (MC). That is, part of the transmitted energymay couple back to the receiver. This mutual coupling is a well-knownissue in the art. Within the processor/DSP circuitry 128, MCcancellation circuitry 322 may provide cancellation of the undesiredenergy coupled between the transmit antenna 210-1 and the receiveantenna 210-2. To remove the MC component from the received signal, theMC cancellation circuitry 322 uses the transmit signal to cancel the MCcomponent. Although not explicitly shown, the MC cancellation can beperformed in a time domain or a frequency domain via the MC cancellationcircuitry 322.

After cancelling the MC, discrete Fourier transform (DFT) circuitry 324may convert the received beat signal to the frequency domain and providesamples of the beat signal in this domain. For example, if 30measurements of the target object 314 are obtained from 30 sequentialtarget object reflections, the output x from the DFT circuitry 324includes x_(i)=[x₁, x₂, . . . , x₃₀] as its output. Here, each samplex_(i) corresponds to a spectrum measured from a single radar reflection.These samples x_(i) may then be sent to feature extraction circuitry326. That is, according to an aspect of this disclosure, one or morefeatures (e.g., M features, as shown in the illustration) may beextracted from the spectra of a sequence of radar samples of a targetobject 314. The extracted features may be utilized for classifying thetarget object as human or non-human, for example, as described furtherbelow. That is, features indicative of micromovements characteristic ofhuman target objects may be utilized for categorization of the targetobject as such.

The M extracted features may then be provided to classificationcircuitry 328. In some examples, the classification circuitry 328 may bea support vector machine (SVM) that utilizes machine learning (ML) toclassify target objects. As described further below, SVM classificationcircuitry 328 may determine distances of the extracted features withrespect to a boundary in a defined feature space. Based on the distancesfrom, and/or the location relative to such a boundary in the definedfeature space, the SVM classification circuitry 328 may then provide adetermination of a categorization of the target object 314, e.g., ashuman or non-human Also as described further below, based on thecategorization of the target object 314, the processor/DSP circuitry 128can generate a transmission parameter that controls one or moretransmission attributes for wireless communication. By specifying thetransmission parameter, the processor/DSP circuitry 128 can, forexample, cause the transceiver 120 to decrease a transmit power if atarget object 314 that is near the electronic device 102 is a human, orincrease the transmit power if the target object 314 is farther awayfrom the electronic device 102 and/or is not a human. For example, thepower amplifier 302 may be dynamically controlled based on the targetobject classification. If the target object 314 is determined to not behuman, the processor 122 can, for example, keep the transmissionparameter unchanged. The transmission parameter can adjust a powerlevel, a beam steering angle, a frequency, a selected antenna or antennaarray, or a communication protocol that is used to transmit an uplinksignal and/or receive a downlink signal. The ability to determine therange to the target object 314 and the category of the target object314, and to control the transceiver 120, enables the processor 122 tobalance performance of the electronic device 102 with compliance orradiation requirements.

The processor/DSP circuitry 128 may also be coupled to the LO circuitry306, which can enable the processor/DSP circuitry 128 to control the LOcircuitry 306 via a mode signal. The mode signal, for example, can causethe LO circuitry 306 to switch between generating reference signals forproximity detection or generating reference signals for wirelesscommunication. In other implementations, the application processor 108(see FIG. 1) can perform one or more of these functions.

Although the wireless transceiver 120 is shown as a direct-conversiontransceiver in FIG. 3, the described techniques can also be applied toother types of transceivers, such as superheterodyne transceivers. Ingeneral, the LO circuitry 306 can be used to perform frequencyconversion between any frequency stage (e.g., between basebandfrequencies and radio frequencies, between intermediate frequencies andradio frequencies, or between baseband frequencies and intermediatefrequencies).

FIG. 4 illustrates a series of three charts generated utilizing anexemplary implementation of an electronic device 102. Each illustratedchart shows data from 30 consecutive captures of reflected radar pulsesover a period of 9 seconds, with samples captured at an interval of 0.3seconds. In each respective chart, the horizontal axis represents time(or sample index), and the vertical axis represents the distance fromthe electronic device, as determined utilizing the range detectionalgorithm generally described above. Further, the shade at any givenpoint represents the energy content of the received signal reflected offthe target object at the corresponding time and distance from theelectronic device. For example, the feature extraction circuitry 326 maydetermine the parameters, including the energy content of the receivedsignal at each target range, among other parameters.

Chart 402 provides a data set corresponding to a stationary, non-humantarget object, such as a coffee mug. As shown, such a static, stationarytarget object is characterized by a relatively static data acrosssamples. Chart 404 illustrates a data set corresponding to a movinghuman hand as a target object. This exhibits significant variations inthe data over time. And chart 406 illustrates a data set correspondingto a human hand as a target object, where the person is holding theirhand stationary. Even when a person attempts to hold perfectlystationary, they cannot eliminate micromovements caused by to smallmuscular movements, breathing, vascular pulse, etc. Because anintegrated FMCW-based radar utilizing mmW spectrum has wavelengths onthe order of 1 cm or smaller, it is capable of detecting very smallmovements, such as 2 or 3 mm, in a target object.

By observing data from various objects such as these, the inventorsrecognized that when the detected target object is of a human nature,the observed data exhibit variations or fluctuations of a variety ofmetrics or features, such as the peak energy of the reflected signal,side lobe variations, etc. And furthermore, by analyzing thesefluctuations over a suitable set of features extracted from a targetobject, the target object can reliably and accurately be categorized aseither a human or non-human target object. According to various aspectsof the present disclosure, a machine learning (ML) algorithm is providedfor utilizing these and other features to categorize target objects. Inthis manner, in some examples transmission characteristics may becontrolled to dynamically meet MPE requirements for mmW transmissions.

FIG. 5 provides two charts illustrating exemplary 2-dimensional (2D)feature spaces. These charts provide examples for how the use ofsuitable sets of extracted features can be combined to improve thereliability of categorizing target objects detected with a radar-basedproximity detector as described above. In the context of the presentdisclosure, a feature refers to a determined parameter that is relevantor specific to the issue of classification of target objects. That is,an electronic device 102 may extract one or more features to determinewhether a target object is human

In the charts shown in FIG. 5, each point corresponds to a data samplecollected from measurements of a target object, post-processing. In thefirst chart 502, the horizontal axis represents the variance, over 30sequential radar reflections, of the peak power less the average powerof the reflected signal. The vertical axis represents the maximum of thediscrete Fourier transform (DFT) of the power of the reflected signalless the average of the DFT of the power of the reflected signal. And inthe second chart 504, the horizontal axis represents the mean, over 30sequential radar reflections, of the change (Δ) in phase between samplen and sample n−1; and the vertical axis represents the variance of thechange (Δ) in phase between sample n and sample n−1.

In each of these charts, each ‘x’ represents a data point from a humantarget object, and each ‘∘’ represents a data point from a non-humantarget object. As can clearly be seen, the circles ‘∘’ representingsamples of non-human target objects form a cluster on the lower leftcorner. On the other hand, the ‘x’es representing samples of humantarget objects are spread across the chart. Thus, in these exemplaryillustrations, a simple linear boundary separation might be used todistinguish between the human and non-human samples.

FIG. 6 illustrates a further example of a 2D feature space showing oneexample of how a classifier algorithm can utilize data from targetobjects of known classification to determine an optimal separationbetween target objects of different categories. In this illustration,the axes labeled x₁ and x₂ represent features extracted from the targetobjects. The data points shown with the filled-in circle (●) eachcorrespond a human target object, and the data points shown with anempty circle (∘) each correspond a non-human target object.

The charts in FIGS. 5 and 6 show a 2D feature space, comparing twoextracted features to obtain a categorization between human andnon-human target objects. However, aspects of this disclosure are notlimited to such a 2D feature space. In general, due to higherdimensionality of the feature space, a many-dimensional comparisonbetween any suitable number of features may be constructed. In such acase, rather than the line 602 being the boundary between classes, aplane or a hyper plane may be utilized to separate the classes of targetobjects within a higher-dimensional feature space. That is, an exemplaryclassification circuitry 328 may utilize an SVM to analyze any suitablenumber of extracted features from a set of samples from a target objectand determine that target object's classification.

Referring again to FIG. 6, it may be observed that the differentcategories of data points can easily be separated from one another.However, for optimal separation of the different categories to mostreliably categorize new data from target objects in the future, acategorization model should identify the best separation between thecategories. For example, an infinite number of lines, such as the line602, could theoretically fully separate the measurements in the givendata set. However, if a categorization model were to utilize theillustrated line 604 to predict the category of future target objects,the prediction may be unreliable. That is, because the line 604 passesclose to the cluster of measured human target objects, even smallvariations in the extracted features of a future measurement of a humantarget object could cause the measurement to fall on the opposite sideof the line 604, resulting in the model miscategorizing the targetobject.

In an aspect of this disclosure, a machine learning (ML) algorithm maybe utilized to establish a reliable separation between sets of targetobjects into distinct categories, e.g., human and non-human targetobjects. That is, a classifier algorithm may be established by buildinga large dataset (e.g., training data) including many human body parts(e.g., hands in different poses, arms, faces, etc.) as well as manynon-human objects commonly encountered by electronic devices.

As one example, the classification circuitry 328 may be a support-vectormachine (SVM), which may be utilized to build a target object classifieralgorithm. SVMs are ML models well-known in the art used forclassification of data sets. Broadly, an SVM may be utilized to analyzea data set and identify a boundary between classes, by maximizing theminimum distance between the closest point in each class's set ofsamples, and the boundary. These boundaries are called support vectors.

Referring once again to FIG. 6, the chart shows data corresponding toeight realizations of human target objects (●), and eight realizationsof non-human target objects (∘). Here, a realization corresponds to aset of radar captures or observances received after reflecting off atarget object, based on a radar transmission. According to an aspect ofthe present disclosure, by virtue of selection of suitable features x₁and x₂ extracted from each realization, it can be observed that the twoclasses of data can be separated into distinct groups. In a furtheraspect, the classification circuitry 328 (e.g., an SVM) can be utilizedto calculate, based on these data, the optimal boundary between theclasses.

As discussed above, in a 2-dimensional feature space as illustrated inFIG. 6, the boundary corresponds to a line. In an aspect of thisdisclosure, this line may be represented by the values where wx−b=0.Here, w is a weight vector; x is a vector <x₁, x₂> within the featurespace; and b is a scalar bias or offset. In this equation, the weightvector w is configured such that the product of the two vectors wxresults in a scalar value.

As seen in FIG. 6, in the 2-dimensional feature space, the separationbetween lines that are parallel to the boundary 602, which cross throughthe closest samples in each class, has the value of

$\frac{2}{w}.$The boundary 602 may be selected to be centered between these respectivelines. Here, ∥w∥ represents the norm of w, calculated as the root of thesum of the squares of all elements of the vector (in the illustratedcase, ∥w∥=√{square root over (x₁ ²+x₂ ²))}. And as further seen in FIG.6,

$\frac{b}{w}$is the distance or offset of the boundary 602 from the origin.

The classification circuitry 328 (e.g., an SVM), based on analysis ofthe data set, determines the values of w and b for the optimal boundary602 between the classes based on the training data given to it. That is,although this illustration shows a total of 16 samples or realizations,with 8 each from human target objects and non-human target objects, anysuitable number of samples may be utilized as a training data set. Ingeneral, the larger and more diverse/varied in nature the training dataused, the more reliable will be the calculated boundary 602 fordetermining the class of new incoming samples.

In general, the classification circuitry 328 may generate a boundary(e.g., a line, a plane, or a hyperplane, depending on the number ofdimensions in the feature space) to separate samples from differentcategories. That is, the SVM defines a boundary that separates thesamples from the different categories such that the minimum distancebetween samples in each category and the boundary is maximized With thisboundary, a robust way to distinguish the different categories can beprovided.

By having such a boundary predetermined, the computational cost for theelectronic device 102 to categorize a new target object can be reduced.That is, a deep learning algorithm or a neural network algorithm couldpotentially determine a separation between categories in real time.However, the use of such an algorithm would come at the cost of highcomputational requirements, high power usage, a long time forcomputation, and even a higher system expense.

The following provides some examples of features that may be useful fordistinguishing a human target object from a non-human target object.According to an aspect of this disclosure, feature extraction circuitry326 may analyze a set of realizations (reflected signals) from a targetobject as described above to extract a set of M features correspondingto that target object.

In some examples, feature extraction circuitry 326 may utilize dynamictime warping (DTW). DTW is an algorithm known in the art for determiningthe similarity between sequences (e.g., sequences X and Y, each having Lsamples), and is defined according to the equation:

${{DTW}( {X,Y} )} = {\min\{ {\sum\limits_{l = 1}^{L}{c( {x_{n_{l}},y_{m_{l}}} )}} \}}$where X={x₁, x₂, . . . , x_(L)}, and Y={y₁, y₂, . . . , y_(L)}. Thus,for each sample in X and each sample in Y, the DTW relies on acomparison of the distance between the respective values in the DFTdomain In general, the DTW of two very similar sequences may be verysmall, whereas the DTW of two very different sequences may be large.Var_(DTW)=var_(n)(DTW_(n))   1.

For example, a feature that the feature extraction circuitry 326 mayextract is the variance of the DTW (Var_(DTW)) over a series ofrealizations (e.g., a series of 10, or any suitable number ofrealizations). In the equation above, Var_(DTW) corresponds to thevariance, across n realizations, of the calculated DTW. For a static(e.g., non-human) object, the variance of the determined DTW over aseries (e.g., a series of 10) of realizations will be very small, aseach realization will provide essentially the same data. On the otherhand, for a human target object, a series (e.g., a series of 10) ofrealizations will have noticeable differences from one another, due tomovements or micromovements of the human target object. Therefore, thevariance of the DTW across the series of realizations would be greaterthan that for non-human target objects.Max_(DTW)=max_(n)(DTW_(n))   2.

In a further example, a feature that the feature extraction circuitry326 may extract is the maximum of the DTW (Max_(DTW)) over a series ofrealizations (e.g., a series of 10, or any suitable number ofrealizations). In the equation above, Max_(DTW) corresponds to themaximum, across n realizations, of the calculated DTW. Here, the maximumof the DTW may be considered as a spread between different realizations.By utilizing the maximum DTW, even if a human target object is fairlystable for, for example, 5 realizations, but then exhibits motion, anelectronic device can determine that the maximum DTW is relatively high.Accordingly, even for a temporarily very stationary target object,strong movements can be utilized to categorize a target object as human.Var_ΔP _(avg_max)=max_(i){Σ_(n)Var[ΔP _(avg) _(peaks) (i, n)]}  3.

In a further example, a feature that the feature extraction circuitry326 may extract is the variance of the difference between the peak powerin each realization, and the average peak power across a sequence ofrealizations. For example, after DFT circuitry 324 may calculate the DFTfor a given realization. This DFT can provide the received power of thatsample at each of a range of frequencies. In an example utilizingFMCW-based radar, these frequencies correspond to the distance from theelectronic device. Accordingly, by plotting the power P vs. thedistance, one or more local maxima or peaks (e.g., i local maxima) mayappear for each respective sample. In this extracted feature, acrossmultiple realizations, an average peak value may be determined. Here,the change (Δ) in the detected peak power level for each sample nrelative to the average peak value may be determined; and acrossmultiple realizations, the variance of this change may be determined.According to an aspect of this disclosure, the value of this feature maybe higher for a human target object than for a non-human target object.Var_ΔD _(peaks_max)=max_(i){Σ_(n)Var[ΔD _(peaks() i, n)]}  4.

In a further example, a feature that the feature extraction circuitry326 may extract is the variance of the difference between the distancewhere the peak power lies in each realization, and the average distancewhere the peak power lies across a sequence of realizations. Thisfeature is very similar to that described above for Var_ΔP_(avg_max).However, here, rather than looking at the measured power, this featurelooks at the measured distance from where that peak power was captured.Similar to the above, the value of this feature may be higher for ahuman target object than for a non-human target object.ΔDFT_(peak)=max_(n)(DFT_(peak_pwr) _(n) )−min_(n)(DFT_(peak_pwr) _(n) )  5.

In a further example, a feature that the feature extraction circuitry326 may extract is the spread (Δ) of the peak power of the DFT of arealization, over a series of n (e.g., a series of 10) realizations.That is, as shown in equation 5 above, the spread is defined as thedifference between the maximum (max) peak power of the DFT and theminimum (min) peak power of the DFT across the series of realizations.With a stationary non-human target object, this spread would be expectedto be relatively small, while with a human target object, exhibiting atleast micromovements, this spread would be expected to be larger.Var_DFT_(peak)=Var(DFT_(peak_pwr) _(n) )   6.

In a further example, a feature that the feature extraction circuitry326 may extract is the variance of the peak power of the DFT across aseries of n (e.g., a series of 10) realizations. With a stationarynon-human target object, this variance would be expected to berelatively small, while with a human target object, exhibiting at leastmicromovements, this variance would be expected to be larger.Var_ΔP _(b2b) _(peaks) =max_(i){Σ₁ ^(n−1)Var[ΔP _(avg) _(peaks) (i,n)]}  7.

In a further example, a feature that the feature extraction circuitry326 may extract is the variance of the change (Δ) in the power measuredin consecutive captures. In equations 1-6 above, the extracted featureshave relied on relationships across full sequences of realizations.However, in equation 7 (and equation 8, below), the extracted featuresrely on relationships between consecutive or sequential individualcaptures or realizations. When a target object is human, due to themicromovements that may occur at any given time, there may be relativelylarge changes in the peak power from one capture to the next. Byutilizing the variance of this parameter as an extracted feature,micromovements characteristic of a human target object can beidentified.Var_ΔD _(b2b) _(peaks) =max_(i){Σ₁ ^(n−1)Var[ΔD _(avg) _(peaks) (i,n)]}  8.

In a further example, a feature that the feature extraction circuitry326 may extract is the variance of the change (Δ) in the distance atwhich the peak power occurs in consecutive or sequential captures orrealizations. When a target object is human, due to micromovements thatmay occur at any given time, there may be relatively substantial changesin the peak power distance from one capture to the next. By utilizingthe variance of this parameter as an extracted feature, micromovementscharacteristic of a human target object can be identified.

In some further examples, the feature extraction circuitry 326 mayextract features based on the in-phase and quadrature (I/Q) samples inthe time domain after removal of the mutual coupling. As one example, asequence of n consecutive time domain samples (e.g., 10 samples) may becollected utilizing an FMCW-based radar as described above. The realpart of the time domain samples may be determined according to thefollowing equation:

${{Re}\{ {IQ}_{avg} \}} = ( {\frac{1}{n}{\sum\limits_{n}{{Re}\{ {IQ}_{n} \}}}} )$And further, the imaginary part of the time domain samples may bedetermined according to the following equation:

${{Im}\{ {IQ}_{avg} \}} = ( {\frac{1}{n}{\sum\limits_{n}{{Im}\{ {IQ}_{n} \}}}} )$

When collecting samples of a human target object, even in the timedomain there may be ‘noise’ or variations in the measured power ofconsecutive samples due, e.g., to micromovements. However, whencollecting samples of a stationary non-human target object the measuredpower of consecutive samples may generally be relatively stable.Accordingly, an electronic device 102 can utilize calculated parameterssuch as the variance of the time domain samples to categorize targetobjects. In a further example, the mean value of the I/Q samples may beremoved (mean removal). For example, the calculated average or meanacross a set of samples may be subtracted from the value of each sample.In this way, any bias or offset that might impact the final result canbe accounted for.

$9.\mspace{20mu}{Var}\mspace{11mu}\{ {\frac{1}{n}{\sum_{n}\lbrack ( {{{Re}( {IQ}_{n} )} - {{Re}( {IQ}_{avg} )}} \rbrack \}}} $

Thus, in one example, a feature that the feature extraction circuitry326 may extract is the variance (Var{ }) of the real part (Re( )) of thetime domain samples (IQ_(n)), with mean removal as described above.

$10.\mspace{14mu}{Var}\{ {\frac{1}{n}{\sum_{n}\lbrack ( {{{Im}( {IQ}_{n} )} - {{Im}( {IQ}_{avg} )}} \rbrack \}}} $

In a further similar example, a feature that the feature extractioncircuitry 326 may extract is the variance of the imaginary part (Im( ))of the time domain samples, with mean removal as described above.

FIG. 7 is a flow chart illustrating an exemplary process for building ahuman target object classifier in accordance with some aspects of thepresent disclosure. In various examples, some or all illustratedfeatures may be omitted in a particular implementation within the scopeof the present disclosure. Further, some illustrated features may not berequired for implementation of a particular example. In some examplesthe process in FIG. 7 may be carried out by a manufacturer, vendor, orretailer of the electronic device 102. In some examples, the process inFIG. 7 may be carried out by any suitable apparatus or means forcarrying out the functions or algorithm described below.

At block 702, a data collection process may be carried out. For example,an electronic device 102 may collect a data set of radar captures onmultiple humans, for example of different gender, ethnicity, age, size,etc. These data may be collected from various body parts of the subjectpeople. Further, the electronic device 102 may collect further data ofradar captures on multiple non-human target objects of various types andcharacteristics. Here, it may be advantageous to maximize the size ofthe data set and the variety of target objects.

At block 704, the data set may be subjected to post-processing toextract features that can be utilized to distinguish human vs. non-humantarget objects. For example, one or more of the features described abovein relation to equations 1-10 may be extracted from the data set, aswell as any other suitable features that are useful for distinguishingthe target objects.

At block 706, a classifier model may be constructed and trained based onthe extracted features and the collected data set. That is, theextracted features may be utilized to train and validate the performanceof a classifier based on the known categorization of the samples in thedata set. And at block 708, an SVM may be established by mapping thefeature space and computing the distance from mapped data points from adetermined boundary (e.g., in a multi-dimensional feature space, ahyper-plane boundary). At block 710, the constructed human classifier'sperformance may be tested for accuracy in real-time based on detectionof unknown (to the classifier) target objects, both human and non-humanWhen the classifier's reliability is suitably high then the classifiermay be deployed to users.

FIG. 8 is a flow chart illustrating an exemplary process for classifyingtarget objects in accordance with some aspects of the presentdisclosure. Though human may be used as an example, any living animalmay be the basis of adjusting transmission power. As described below,some or all illustrated features may be omitted in a particularimplementation within the scope of the present disclosure, and someillustrated features may not be required for implementation of allembodiments. In some examples, the process may be carried out by theelectronic device 102 illustrated in FIG. 1 or 2. In some examples, theprocess may be carried out by the various components of the electronicdevice, including but not limited to a transceiver 120 and processor orDSP circuitry 122 as illustrated in FIG. 3. In some examples, theprocess of FIG. 8 may be carried out by any suitable apparatus or meansfor carrying out the functions or algorithm described below.

At block 802, an electronic device 102 may transmit a detection signal.For example, a transceiver 120 may utilize one or more antennas, such asa transmit antenna 210-1 to transmit a pulse, an FMCW signal, amulti-tone signal, or any other suitable signal for radar-basedproximity detection. At block 804, the electronic device 102 may receivea reflection signal reflected from a target object. For example, thetransceiver 120 may utilize one or more antennas, such as a receiveantenna 210-2 to receive the reflection signal.

At block 806, the electronic device 102 may extract one or more featuresof the reflection signal. For example, feature extraction circuitry 326may process information corresponding to the reflection signal, such asthe spectra of one or more radar samples of the reflection signal, todetermine one or more features useful for characterizing the targetobject. In some examples, a given feature may correspond to a singlerealization, or reflection from the target object. In other examples, agiven feature may correspond to a plurality of realizations, such as asequence of any suitable number of realizations.

At block 808, the electronic device 102 may apply the extracted featuresto a classification model. For example, an SVM 810 may be configuredaccording to a set of training data 812 to establish one or moreboundaries 814. The boundary or boundaries may be configured to separateclasses of target objects in a feature space, based on featuresextracted from reflection signals received off target objects. Theestablishment of the boundary 814 based on the training data 812 may beaccording to a classification model 816 established utilizing theprocess described above and illustrated in FIG. 7.

At block 818, the electronic device 102 may determine, based on one ormore features of the reflection signal, a category of the target object.For example, the electronic device 102 may determine a location, withinthe feature space of the classification model 816, of the target objectrelative to the boundary 814. With this location, the electronic device102 may identify a category of the target object based on the locationwithin the feature space (e.g., on which side of the boundary 814 doesthe target object lie within the feature space).

If the category of the target object indicates that the target object ishuman, then at block 820, the electronic device 102 may adjust at leastone transmission parameter of a transmission signal, such as a mmWuplink signal, to provide no greater than a maximum permissible exposure(MPE) of the mmW signal to the human target object. For example, theelectronic device 102 may adjust at least one of a power level of theuplink signal, a beam steering angle of the uplink signal, a frequencyof the uplink signal, a selected antenna of the uplink signal, acommunication protocol of the uplink signal, or a combination of theabove, such that the power of the uplink signal at the human targetobject is no greater than the MPE regulatory requirements. On the otherhand, if the category of the target object indicates that the targetobject is non-human, then at block 822 the electronic device 102 mayadjust at least one transmission parameter of a transmission signalwithout taking MPE regulations into account. For example, the electronicdevice may adjust the transmission parameter(s) in such a way that thepower of the transmitted signal may exceed an MPE level at the non-humantarget object. At block 824, the electronic device 102 may transmit anadjust signal using the adjusted transmission parameter, as describedabove.

The process shown in FIG. 8 may include additional aspects, such as anysingle aspect or any combination of aspects described below and/or inconnection with one or more other processes described elsewhere herein.

In a first aspect, an electronic device may adjust one or moretransmission parameters based on a determined category of a targetobject. Here, the transmission parameter may be at least one of a powerlevel, a beam steering angle, a frequency, a selected antenna, acommunication protocol, or some combination of the above.

In a second aspect, alone or in combination with the first aspect, thecategory of the target object may be one of: a human target object, or anon-human target object; an animal target object, or a non-animal targetobject; or a living target object, or a non-living target object.

In a third aspect, alone or in combination with one or more of the firstand second aspects, an electronic device may determine, based on one ormore features of a reflection signal, a category of a target object.Here, the one or more features of the reflection signal may include oneor more features indicative of micromovements characteristic of a humantarget object.

In a fourth aspect, alone or in combination with one or more of thefirst through third aspects, the determining the category of the targetobject may include extracting one or more features of a reflectionsignal, applying the one or more extracted features to a classificationmodel configured with a boundary that separates objects, within afeature space, into categories, determining a location of the targetobject, within the feature space, relative to the boundary, andidentifying the category of the target object based on the locationwithin the feature space.

In a fifth aspect, alone or in combination with one or more of the firstthrough fourth aspects, the classification model corresponds to asupport vector machine (SVM). Here, the boundary within the featurespace is determined based on a set of training data.

In a sixth aspect, alone or in combination with one or more of the firstthrough fifth aspects, the adjusted signal includes a millimeter-wave(mmW) signal. Here, the category of the target object is a human targetobject, and the adjusting at least one transmission parameter includesconfiguring the adjusted signal to provide no greater than a maximumpermissible exposure (MPE) of the mmW signal to the human target object.

In a seventh aspect, alone or in combination with one or more of thefirst through sixth aspects, the one or more features of the reflectionsignal include at least one of: a variance of a dynamic time warping ofa series of realizations sampling the target object; a maximum of thedynamic time warping of the series of realizations sampling the targetobject; a variance of a difference between a peak power in eachrealization of the series of realizations sampling the target object,and an average peak power across the series of realizations sampling thetarget object; a variance of a difference between a distance where apeak power lies in each realization of the series of realizationssampling the target object, and an average distance where the peak powerlies in the series of realizations sampling the target object; a spreadof peak power of a discrete Fourier transform (DFT) of the series ofrealizations sampling the target object; a variance of the peak power ofthe DFT of the series of realizations sampling the target object; avariance of a change in power measured in consecutive realizationssampling the target object; a variance of a distance at which a peakpower occurs in consecutive realizations sampling the target object; avariance of a real part of time domain samples of a realization samplingthe target object; a variance of an imaginary part of time domainsamples of the realization sampling the target object; or combinationsthereof.

In an eighth aspect, alone or in combination with one or more of thefirst through seventh aspects, the electronic device, or a wirelesscommunication device, includes a housing shaped and sized to carry oneor more components, including a memory, a wireless transceiver, a poweramplifier, and at least one processor. Here, the memory stores dataconfigured to enable the at least one processor to processobject-sensing information against stored data, thereby enablingclassification of one or more objects into one or more object-typeclasses.

In a ninth aspect, alone or in combination with one or more of the firstthrough eighth aspects, the wireless transceiver is configured to engagein mmW communication and mmW object sensing substantiallysimultaneously.

In a tenth aspect, alone or in combination with one or more of the firstthrough ninth aspects, the wireless communication device includes anantenna module having an array of antenna elements, wherein the poweramplifier is configured to dynamically provide power to selected ones ofthe antenna elements for moderation of the transmission parameter and/orfor beamforming.

In an eleventh aspect, alone or in combination with one or more of thefirst through tenth aspects, the wireless communication device includesan interface circuit for interfacing with an auxiliary device spacedapart from the housing. The interface circuit is configured to enablecommunication between the wireless communication device and theauxiliary device such that the wireless communication device receivesobject-sensing information from the auxiliary device, and in response,moderates a transmission parameter associated with the wirelesstransceiver transmitting and/or receiving millimeter waves.

In a twelfth aspect, alone or in combination with one or more of thefirst through eleventh aspects, the at least one processor is configuredto determine whether sensed objects can be associated with one or moreobject classes, wherein the one or more object classes comprise:non-human tissue, human tissue, or a combination thereof.

In a thirteenth aspect, alone or in combination with one or more of thefirst through twelfth aspects, the wireless transceiver is configured tosense objects ranging from about 1 to about 360 degrees such that the atleast one processor can yield an object-sensing map of objects exteriorto said housing.

In a fourteenth aspect, alone or in combination with one or more of thefirst through thirteenth aspects, the at least one processor isconfigured to produce a map based at least on the object-sensinginformation, wherein the map identifies objects relative to the wirelesscommunication device.

In a fifteenth aspect, alone or in combination with one or more of thefirst through fourteenth aspects, the wireless transceiver is configuredto sense objects relative to and exterior the housing via mmW signalingvia repetitive transmission of millimeter wave signals toward one ormore objects and repetitive receipt of mmW signals from the one or moreobjects such that the wireless transceiver is configured to observemicromovements occurring by the one or more objects.

In a sixteenth aspect, alone or in combination with one or more of thefirst through fifteenth aspects, the wireless communication devicereceives signaling from one or more other wireless communication devicesindicating that any one of the other wireless communications hasmoderated one or more wireless transmission parameters based on theobject-class information.

In a seventeenth aspect, alone or in combination with one or more of thefirst through sixteenth aspects, the wireless communication devicedetermines surrounding information of objects located around the one ormore other wireless communication devices.

In an eighteenth aspect, alone or in combination with one or more of thefirst through seventeenth aspects, the wireless transmission parameterrelates to power of transmitted or received millimeter-wave signals.

In a nineteenth aspect, alone or in combination with one or more of thefirst through eighteenth aspects, the wireless communication device isconfigured as a vehicle. Here, the at least one processor is configuredto control one or more operating parameters of the vehicle body based atleast in part on the object-sensing information.

In a twentieth aspect, alone or in combination with one or more of thefirst through nineteenth aspects, the wireless communication device isconfigured for gaming Here, one or more user interfaces are positionedproximate a housing of the gaming device, and the at least one processoris further configured to receive user input via the one or more userinterfaces positioned proximate the housing and in response to conveyobject sensing information to a user.

In a twenty-first aspect, alone or in combination with one or more ofthe first through twentieth aspects, the gaming device includes avisible interface field defining a visual display configured to visuallyconvey object sensing information to the user.

In one configuration, an electronic device 102 includes means fortransmitting a detection signal, means for receiving a reflection signalreflected from a target object, and means for transmitting an adjustedsignal using a transmission parameter. In one aspect, the aforementionedmeans may be the transceiver 120. In one aspect, the aforementionedmeans may be the processor(s) or DSP circuitry 128 shown in FIGS. 1 and3, configured to perform the functions recited by the aforementionedmeans. In another aspect, the aforementioned means may be a circuit orany apparatus configured to perform the functions recited by theaforementioned means. The electronic device 102 may further includemeans for determining, based on one or more features of a reflectionsignal, a category of the target object, and means for adjusting atleast one transmission parameter based on the category of the targetobject. In one aspect, the aforementioned means may be the processor(s)or DSP circuitry 128 shown in FIGS. 1 and 3, configured to perform thefunctions recited by the aforementioned means. In another aspect, theaforementioned means may be a circuit or any apparatus configured toperform the functions recited by the aforementioned means.

Of course, in the above examples, the circuitry included in theprocessor or DSP circuitry 128 is merely provided as an example, andother means for carrying out the described functions may be includedwithin various aspects of the present disclosure, including but notlimited to the instructions stored in the computer-readable storagemedium 110, or any other suitable apparatus or means described in anyone of the FIGS. 1, 2, and/or 3, and utilizing, for example, theprocesses and/or algorithms described herein in relation to FIG. 8.

Within the present disclosure, the word “exemplary” is used to mean“serving as an example, instance, or illustration.” Any implementationor aspect described herein as “exemplary” is not necessarily to beconstrued as preferred or advantageous over other aspects of thedisclosure. Likewise, the term “aspects” does not require that allaspects of the disclosure include the discussed feature, advantage ormode of operation. The term “coupled” is used herein to refer to thedirect or indirect coupling between two objects. For example, if objectA physically touches object B, and object B touches object C, thenobjects A and C may still be considered coupled to one another—even ifthey do not directly physically touch each other. For instance, a firstobject may be coupled to a second object even though the first object isnever directly physically in contact with the second object. The terms“circuit” and “circuitry” are used broadly, and intended to include bothhardware implementations of electrical devices and conductors that, whenconnected and configured, enable the performance of the functionsdescribed in the present disclosure, without limitation as to the typeof electronic circuits, as well as software implementations ofinformation and instructions that, when executed by a processor, enablethe performance of the functions described in the present disclosure.

One or more of the components, steps, features and/or functionsillustrated in FIGS. 1-8 may be rearranged and/or combined into a singlecomponent, step, feature or function or embodied in several components,steps, or functions. Additional elements, components, steps, and/orfunctions may also be added without departing from novel featuresdisclosed herein. The apparatus, devices, and/or components illustratedin FIGS. 1-8 may be configured to perform one or more of the methods,features, or steps described herein. The novel algorithms describedherein may also be efficiently implemented in software and/or embeddedin hardware.

It is to be understood that the specific order or hierarchy of steps inthe methods disclosed is an illustration of exemplary processes. Basedupon design preferences, it is understood that the specific order orhierarchy of steps in the methods may be rearranged. The accompanyingmethod claims present elements of the various steps in a sample order,and are not meant to be limited to the specific order or hierarchypresented unless specifically recited therein.

The previous description is provided to enable any person skilled in theart to practice the various aspects described herein. Variousmodifications to these aspects will be readily apparent to those skilledin the art, and the generic principles defined herein may be applied toother aspects. Thus, the claims are not intended to be limited to theaspects shown herein, but are to be accorded the full scope consistentwith the language of the claims, wherein reference to an element in thesingular is not intended to mean “one and only one” unless specificallyso stated, but rather “one or more.” Unless specifically statedotherwise, the term “some” refers to one or more. A phrase referring to“at least one of” a list of items refers to any combination of thoseitems, including single members. As an example, “at least one of: a, b,or c” is intended to cover: a; b; c; a and b; a and c; b and c; and a, band c. All structural and functional equivalents to the elements of thevarious aspects described throughout this disclosure that are known orlater come to be known to those of ordinary skill in the art areexpressly incorporated herein by reference and are intended to beencompassed by the claims. Moreover, nothing disclosed herein isintended to be dedicated to the public regardless of whether suchdisclosure is explicitly recited in the claims. No claim element is tobe construed under the provisions of 35 U.S.C. § 112(f) unless theelement is expressly recited using the phrase “means for” or, in thecase of a method claim, the element is recited using the phrase “stepfor.”

What is claimed is:
 1. A wireless communication method comprising:obtaining a plurality of realizations sampling at least one objectproximate a transmitter by performing, for each realization of theplurality of realizations: transmitting, via the transmitter, aradar-based proximity detection signal toward the at least one object;and receiving a reflection signal reflected from the at least oneobject; determining, based on variations over time in one or morefeatures of the plurality of realizations, a category of the at leastone object; adjusting at least one transmission parameter based on thecategory of the at least one object; and transmitting a wirelesscommunication signal via the transmitter using the at least onetransmission parameter such that the wireless communication signal takesinto account the category of the at least one object.
 2. The method ofclaim 1, wherein the at least one transmission parameter comprises atleast one of a power level, a beam steering angle, a frequency, aselected antenna, a communication protocol, or a combination thereof. 3.The method of claim 1, further comprising determining that the at leastone object is one of: a human, or a non-human target object; an animal,or a non-animal target object; or a living target object, or anon-living target object.
 4. The method of claim 1, wherein thevariations in the one or more features of the plurality of realizationscomprise one or more features indicative of micromovementscharacteristic of a human.
 5. The method of claim 1, wherein thedetermining the category of the at least one object comprises:extracting the one or more features of the plurality of realizations;applying the one or more extracted features to a classification modelconfigured with a boundary that separates objects, within a featurespace, into categories; determining a location of the at least oneobject, within the feature space, relative to the boundary; andidentifying the category of the at least one object based on thelocation within the feature space.
 6. The method of claim 1, wherein thewireless communication signal comprises a millimeter-wave signal,wherein the category of the at least one object is a human, and whereinthe adjusting at least one transmission parameter comprises configuringthe wireless communication signal to provide no greater than a maximumpermissible exposure of the millimeter-wave signal to the human.
 7. Themethod of claim 1, wherein the variations over time in the one or morefeatures of the plurality of realizations comprise at least one of: avariance of a dynamic time warping of the plurality of realizations; amaximum of the dynamic time warping of the plurality of realizations; avariance of a difference between a peak power in each realization of theplurality of realizations, and an average peak power across theplurality of realizations; a variance of a difference between a distancewhere a peak power lies in each realization of the plurality ofrealizations, and an average distance where the peak power lies in theplurality of realizations; a spread of peak power of a discrete Fouriertransform (DFT) of the plurality of realizations; a variance of the peakpower of the DFT of the plurality of realizations; a variance of achange in power measured in consecutive realizations of the plurality ofrealizations; a variance of a distance at which a peak power occurs inconsecutive realizations of the plurality of realizations; a variance ofa real part of time domain samples of the plurality of realizations; avariance of an imaginary part of time domain samples of the plurality ofrealizations; or combinations thereof.
 8. The method of claim 1, whereinthe method is operable at an electronic device, the method furthercomprising: sensing the at least one object, relative to the electronicdevice; and conveying information associated with the at least oneobject positioning relative to the electronic device.
 9. An electronicdevice comprising: a processor; a transceiver communicatively coupled tothe processor; and a data storage medium communicatively coupled to theprocessor, wherein the processor is configured for: obtaining aplurality of realizations sampling at least one object proximate thetransmitter by performing, for each realization of the plurality ofrealizations: transmitting, via the transceiver, a radar-based proximitydetection signal toward the at least one object; and receiving areflection signal via the transceiver, the reflection signal reflectedfrom the at least one object; determining, based on variations over timein one or more features of the plurality of realizations, a category ofthe at least one object; adjusting at least one transmission parameterbased on the category of the at least one object; and transmitting, viathe transceiver, a wireless communication signal using the at least onetransmission parameter such that the wireless communication signal takesinto account the category of the at least one object.
 10. The electronicdevice of claim 9, wherein the at least one transmission parametercomprises at least one of a power level, a beam steering angle, afrequency, a selected antenna, a communication protocol, or acombination thereof, wherein the one or more features of the pluralityof realizations comprise one or more features indicative ofmicromovements characteristic of a human, and the processor furtherconfigured for determining that the at least one object is one of: ahuman, or a non-human target object; an animal, or a non-animal targetobject; or a living target object, or a non-living target object. 11.The electronic device of claim 9, wherein the processor, beingconfigured for determining the category of the at least one object, isfurther configured for: extracting the one or more features of theplurality of realizations; applying the one or more extracted featuresto a classification model configured with a boundary that separatesobjects, within a feature space, into categories; determining a locationof the at least one object, within the feature space, relative to theboundary; and identifying the category of the at least one object basedon the location within the feature space.
 12. The electronic device ofclaim 9, wherein the wireless communication signal comprises amillimeter-wave signal, wherein the category of the at least one objectis a human, and wherein the processor, being configured for adjusting atleast one transmission parameter, is further configured for adjustingthe wireless communication signal to provide no greater than a maximumpermissible exposure of the millimeter-wave signal to the human.
 13. Theelectronic device of claim 9, wherein the one or more features of theplurality of realizations comprise at least one of: a variance of adynamic time warping of the plurality of realizations; a maximum of thedynamic time warping of the plurality of realizations; a variance of adifference between a peak power in each realization of the plurality ofrealizations, and an average peak power across the plurality ofrealizations; a variance of a difference between a distance where a peakpower lies in each realization of the plurality of realizations, and anaverage distance where the peak power lies in the plurality ofrealizations; a spread of peak power of a discrete Fourier transform(DFT) of the plurality of realizations; a variance of the peak power ofthe DFT of the plurality of realizations; a variance of a change inpower measured in consecutive realizations of the plurality ofrealizations; a variance of a distance at which a peak power occurs inconsecutive realizations of the plurality of realizations; a variance ofa real part of time domain samples of the plurality of realizations; avariance of an imaginary part of time domain samples of the plurality ofrealizations; or combinations thereof.
 14. The electronic device ofclaim 9, wherein the processor is further configured for utilizing thetransceiver for sensing the at least one object, relative to theelectronic device, and for conveying information associated with the atleast one object positioning relative to the electronic device.
 15. Awireless communication device, comprising: a housing shaped and sized tocarry one or more components including a memory, a wireless transceiver,a power amplifier, and at least one processor; the wireless transceiverconfigured to transmit and/or receive millimeter wave signals via awireless channel; the wireless transceiver further configured to utilizea radar-based proximity detection signal to obtain a plurality ofrealizations sampling each of one or more objects exterior the housingvia millimeter wave signaling; and the at least one processor configuredto determine a category of each of the one or more objects based onvariations over time in one or more features of the plurality ofrealizations; wherein the at least one processor is configured tocontrol the power amplifier to moderate a transmission parameterassociated with the wireless transceiver transmitting and/or receivingmillimeter waves based on the category of the one or more objects, andconfigured to convey information associated with the one or more objectspositioning relative to and exterior the housing.
 16. The wirelesscommunication device of claim 15, wherein the wireless transceiver isconfigured to engage in millimeter-wave communication andmillimeter-wave object sensing substantially simultaneously.
 17. Thewireless communication device of claim 15, further comprising an antennamodule having an array of antenna elements, wherein the power amplifieris configured to dynamically provide power to selected ones of theantenna elements for moderation of the transmission parameter and/or forbeamforming.
 18. The wireless communication device of claim 15, furthercomprising an interface circuit for interfacing with an auxiliary devicespaced apart from the housing, the interface circuit configured toenable communication between the wireless communication device and theauxiliary device such that the wireless communication device receivesthe information relating to the category of the one or more objects fromthe auxiliary device, and in response, moderates the transmissionparameter associated with the wireless transceiver transmitting and/orreceiving millimeter waves.
 19. The wireless communication device ofclaim 15, wherein the at least one processor is configured to determinewhether the one or more objects can be associated with one or moreobject classes, wherein the one or more object classes comprise:non-human tissue, human tissue, or a combination thereof.
 20. Thewireless communication device of claim 15, wherein the wirelesstransceiver is further configured to sense the one or more objectsranging from about 1 to about 360 degrees such that the at least oneprocessor can yield an object-sensing map of the one or more objectsexterior to said housing.
 21. The wireless communication device of claim15, wherein the at least one processor is configured to produce a mapbased at least on the information associated with the one or moreobjects positioning, wherein the map identifies the one or more objectsrelative to the wireless communication device.
 22. The wirelesscommunication device of claim 15, wherein the wireless transceiver isconfigured to sense the one or more objects relative to and exterior thehousing via millimeter wave signaling via repetitive transmission ofmillimeter wave signals toward the sensed objects and repetitive receiptof millimeter-wave signals from the-one or more objects such that thewireless transceiver is configured to observe micromovements occurringby the one or more objects.
 23. The wireless communication device ofclaim 15, wherein the at least one processor is further configured to:extract the one or more features of the plurality of realizations; applythe one or more extracted features to a classification model configuredwith a boundary that separates the one or more objects, within a featurespace, into categories; determine locations of the one or more objects,within the feature space, relative to the boundary; and identify thecategories of the one or more objects based on their locations withinthe feature space.
 24. A wireless communication device comprising: ahousing shaped and sized to carry one or more components including amemory and at least one processor; a wireless communication interfacesized and shaped to be placed in a location proximate or within thehousing; the wireless communication interface configured to transmitand/or receive millimeter wave signals via a wireless channel; thewireless communication interface further configured to utilize aradar-based proximity detection signal to obtain a plurality ofrealizations sampling each of one or more objects exterior the housingvia millimeter wave signaling; and the at least one processor configuredto determine a category of each of the one or more objects based onvariations over time in one or more features of the plurality ofrealizations; wherein the at least one processor is configured tocontrol a transmission parameter associated with the wirelesscommunication interface transmitting and/or receiving millimeter wavesbased on the category of the one or more objects, and configured toconvey information associated with the one or more objects relative tothe housing.
 25. The wireless communication device of claim 24, whereinthe wireless communication device is configured as a vehicle, whereinthe housing comprises a vehicle body configured to carry at least one ofa payload or a passenger, and wherein the at least one processor isconfigured to control one or more operating parameters of the vehiclebody based at least in part on the category of the one or more objects.26. The wireless communication device of claim 24, wherein the wirelesscommunication device is configured for gaming, wherein the housing issized and shaped for gaming to allow a user to participate in anelectronic gaming environment; the wireless communication device furthercomprising one or more user interfaces positioned proximate the housing,wherein the at least one processor is further configured to receive userinput via the one or more user interfaces positioned proximate thehousing and in response, to convey object sensing information to theuser.
 27. The wireless communication device of claim 26, furthercomprising a visible interface field defining a visual displayconfigured to visually convey the object sensing information to theuser.